{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 1000x400 with 0 Axes>"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "%matplotlib inline\n",
    "%config InlineBackend.figure_format = 'svg'\n",
    "plt.rc('font', family='Times New Roman', weight='normal', size=12)\n",
    "plt.figure(figsize=(10, 4))\n",
    "\n",
    "font_dict={\"family\": \"KaiTi\", \"size\": 15, \"color\": \"r\"}\n",
    "ACM_Blue = '#0182ac'\n",
    "ACM_Yellow = '#ffd600'\n",
    "ACM_Orange = '#fc9200'\n",
    "ACM_Red = '#e33532'\n",
    "ACM_Light_Blue = '#90cef2'\n",
    "ACM_Green = '#adbf00'\n",
    "ACM_Green = 'g'\n",
    "ACM_Purple = '#78006b'\n",
    "ACM_Dark_Blue = '#184c8c'\n",
    "city_colors = ['C0', 'C2', 'C3', 'C4', 'C5', 'C6', 'C7', 'C8', 'C9', 'C1']\n",
    "line_colors = [ACM_Blue, ACM_Red, ACM_Green, ACM_Orange, ACM_Purple, ACM_Yellow, ACM_Dark_Blue, 'k', 'C9', 'C1',\n",
    "               ACM_Blue, ACM_Red, ACM_Green, ACM_Orange, ACM_Purple, ACM_Yellow, ACM_Dark_Blue, 'k', 'C9', 'C1']\n",
    "line_styles = ['-', '-', '-.', '-.', '--', '--']\n",
    "line_marker = ['o', 's', '^', '*', 'X', 'v']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "./results/2023-03-28 created.\n"
     ]
    },
    {
     "data": {
      "text/plain": "True"
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import os\n",
    "from time import strftime, localtime\n",
    "\n",
    "def make_dir(path):\n",
    "    path = path.strip().rstrip(\"\\\\\")\n",
    "    isExists = os.path.exists(path)\n",
    "    if not isExists:\n",
    "        os.makedirs(path)\n",
    "        print(path + ' created.')\n",
    "        return True\n",
    "    else:\n",
    "        print(path + ' already exists.')\n",
    "        return False\n",
    "\n",
    "save_path = rf'./results/{strftime(\"%Y-%m-%d\", localtime())}'\n",
    "make_dir(save_path)"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "outputs": [],
   "source": [
    "def visualize(results: list, name, precision=10):\n",
    "    if type(results) is not list:\n",
    "        results = [results]\n",
    "    plt.clf()\n",
    "    for i, result in enumerate(results):\n",
    "        result.evo_list = result.evo_list[::int(len(result.evo_list)/precision)]\n",
    "        plt.plot(range(len(result.evo_list)), result.evo_list, label=f'{result.name}: {result.evo_list[-1]:.2f}',\n",
    "                 c=line_colors[i], linestyle=line_styles[i], marker=line_marker[i])\n",
    "    plt.xlabel('迭代次数x100', fontdict={\"family\": \"KaiTi\", \"size\": 15, \"color\": \"k\"})\n",
    "    plt.ylabel('总路程', fontdict={\"family\": \"KaiTi\", \"size\": 15, \"color\": \"k\"})\n",
    "    ax = plt.gca()\n",
    "    ax.ticklabel_format(style='sci', scilimits=(-1,2), axis='y')\n",
    "    ax.get_yaxis().get_offset_text().set(va='bottom', ha='left')\n",
    "    ax.yaxis.get_offset_text().set_fontsize(12)\n",
    "    plt.legend()\n",
    "    plt.title(f'{results[0].data.name}: ')\n",
    "    plt.tight_layout()\n",
    "    plt.savefig(rf'{save_path}/{name}.png', dpi=600)\n",
    "    plt.show()"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "ACO on att48_m2 trial 1:   0%|          | 0/1000 [00:00<?, ?it/s, best: 51246.07]"
     ]
    },
    {
     "data": {
      "text/plain": "<Figure size 1200x300 with 4 Axes>",
      "image/svg+xml": "<?xml version=\"1.0\" encoding=\"utf-8\" standalone=\"no\"?>\n<!DOCTYPE svg PUBLIC \"-//W3C//DTD SVG 1.1//EN\"\n  \"http://www.w3.org/Graphics/SVG/1.1/DTD/svg11.dtd\">\n<svg xmlns:xlink=\"http://www.w3.org/1999/xlink\" width=\"692.048128pt\" height=\"210.234846pt\" viewBox=\"0 0 692.048128 210.234846\" xmlns=\"http://www.w3.org/2000/svg\" version=\"1.1\">\n <metadata>\n  <rdf:RDF xmlns:dc=\"http://purl.org/dc/elements/1.1/\" xmlns:cc=\"http://creativecommons.org/ns#\" xmlns:rdf=\"http://www.w3.org/1999/02/22-rdf-syntax-ns#\">\n   <cc:Work>\n    <dc:type rdf:resource=\"http://purl.org/dc/dcmitype/StillImage\"/>\n    <dc:date>2023-03-28T15:58:51.211364</dc:date>\n    <dc:format>image/svg+xml</dc:format>\n    <dc:creator>\n     <cc:Agent>\n      <dc:title>Matplotlib v3.6.2, https://matplotlib.org/</dc:title>\n     </cc:Agent>\n    </dc:creator>\n   </cc:Work>\n  </rdf:RDF>\n </metadata>\n <defs>\n  <style type=\"text/css\">*{stroke-linejoin: round; stroke-linecap: butt}</style>\n </defs>\n <g id=\"figure_1\">\n  <g id=\"patch_1\">\n   <path d=\"M 0 210.234846 \nL 692.048128 210.234846 \nL 692.048128 0 \nL 0 0 \nz\n\" style=\"fill: #ffffff\"/>\n  </g>\n  <g id=\"axes_1\">\n   <g id=\"patch_2\">\n    <path d=\"M 10.7 185.135471 \nL 207.641176 185.135471 \nL 207.641176 18.815471 \nL 10.7 18.815471 \nz\n\" style=\"fill: #ffffff\"/>\n   </g>\n   <g id=\"PathCollection_1\">\n    <path d=\"M 174.946983 135.406626 \nC 174.946983 135.406626 174.946983 135.406626 174.946983 135.406626 \nC 174.946983 135.406626 174.946983 135.406626 174.946983 135.406626 \nC 174.946983 135.406626 174.946983 135.406626 174.946983 135.406626 \nC 174.946983 135.406626 174.946983 135.406626 174.946983 135.406626 \nC 174.946983 135.406626 174.946983 135.406626 174.946983 135.406626 \nC 174.946983 135.406626 174.946983 135.406626 174.946983 135.406626 \nC 174.946983 135.406626 174.946983 135.406626 174.946983 135.406626 \nC 174.946983 135.406626 174.946983 135.406626 174.946983 135.406626 \nz\n\" clip-path=\"url(#p335aecfa8c)\" style=\"fill: #3e4145; 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     "text": [
      "ACO on att48_m2 trial 1:   0%|          | 2/1000 [00:00<05:05,  3.26it/s, best: 51246.07]"
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     "text": [
      "ACO on att48_m2 trial 1:   0%|          | 4/1000 [00:01<04:46,  3.48it/s, best: 50916.34]"
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    },
    {
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     "text": [
      "ACO on att48_m2 trial 1:   0%|          | 5/1000 [00:01<05:05,  3.26it/s, best: 49980.11]"
     ]
    },
    {
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     "text": [
      "ACO on att48_m2 trial 1:   1%|          | 6/1000 [00:01<04:54,  3.37it/s, best: 49980.11]"
     ]
    },
    {
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     "text": [
      "ACO on att48_m2 trial 1:   1%|          | 7/1000 [00:02<04:48,  3.44it/s, best: 49980.11]"
     ]
    },
    {
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     "text": [
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     "text": [
      "ACO on att48_m2 trial 1:   1%|          | 10/1000 [00:02<04:30,  3.65it/s, best: 48930.08]"
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     "text": [
      "ACO on att48_m2 trial 1:   1%|          | 11/1000 [00:03<04:31,  3.64it/s, best: 48930.08]"
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    },
    {
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     "text": [
      "ACO on att48_m2 trial 1:   1%|          | 12/1000 [00:03<05:09,  3.20it/s, best: 48930.08]"
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    },
    {
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     "text": [
      "ACO on att48_m2 trial 1:   1%|▏         | 13/1000 [00:03<05:01,  3.27it/s, best: 48930.08]\n",
      "\n",
      "KeyboardInterrupt\n",
      "\n"
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   ],
   "source": [
    "from VCMTSP import Instance\n",
    "from ACO import ACO\n",
    "from VNS import VNS\n",
    "from PGA import PGA\n",
    "from IWO import IWO\n",
    "from ABC import ABC\n",
    "\n",
    "cases = [['att48', 2, 4, 6, 8],\n",
    "         ['eil51', 2, 4, 6, 8],\n",
    "         ['kroA100', 2, 4, 6, 8],\n",
    "         ['eil101', 2, 4, 6, 8],\n",
    "         ['kroA150', 10, 20, 30, 40],\n",
    "         ['kroA200', 10, 20, 30, 40],\n",
    "         ['gr202', 10, 20, 30, 40],\n",
    "         ['gr229', 10, 20, 30, 40],\n",
    "         ['gr431', 40, 60, 80, 100],\n",
    "         ['gr666', 40, 60, 80, 100]]"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [],
   "source": [
    "iteration = 1000\n",
    "result_grid = []\n",
    "for case in cases[:7]:\n",
    "    for m in case[1:]:\n",
    "        case_name = f'{case[0]}-m{m}'\n",
    "        data = Instance(file=f'../../data/TSPLIB_VC/{case[0]}.tsp/{case[0]}.tsp.txt', m=m)\n",
    "        print(f'========================={case_name}=========================')\n",
    "        aco = ACO(data=data)\n",
    "        aco.optimize(iteration, diffuse=False)\n",
    "        vns = VNS(data=data)\n",
    "        vns.optimize(iteration)\n",
    "        pga = PGA(data=data)\n",
    "        pga.optimize(iteration)\n",
    "        iwo = IWO(data=data)\n",
    "        iwo.optimize(iteration)\n",
    "        abc = ABC(data=data)\n",
    "        abc.optimize(iteration)\n",
    "        visualize([vns, pga, iwo, abc, aco], name=case_name)\n",
    "        print(f'{vns.optima.total:.2f} {aco.optima.total:.2f} {pga.optima.total:.2f} {iwo.optima.total:.2f} {abc.optima.total:.2f}')\n",
    "        result_grid.append([vns.optima.total, aco.optima.total, pga.optima.total, iwo.optima.total, abc.optima.total])\n",
    "result_grid = np.array(result_grid)\n",
    "pd.DataFrame(result_grid).to_csv(rf'{save_path}/result_grid.csv')"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [],
   "source": [
    "\n"
   ],
   "metadata": {
    "collapsed": false
   }
  }
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